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fbst: An R package for the Full Bayesian Significance Test for testing a sharp null hypothesis against its

Riko Kelter1

  • 1Department of Mathematics, University of Siegen, Walter-Flex-Street 3, 57072, Siegen, Germany. riko.kelter@uni-siegen.de.

Behavior Research Methods
|September 2, 2021
PubMed
Summary

The Full Bayesian Significance Test (FBST) offers a robust Bayesian alternative to traditional null hypothesis significance testing (NHST) and p-values in cognitive sciences. The fbst R package facilitates this method, providing the e-value for evidence against null hypotheses.

Keywords:
Bayesian hypothesis testingFull Bayesian Significance TestNull hypothesis significance testing (NHST)e value

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Area of Science:

  • Psychology and Cognitive Sciences
  • Statistical Modeling
  • Bayesian Inference

Background:

  • Null hypothesis significance testing (NHST) and p-values are standard in psychology but face criticism.
  • Existing alternatives to NHST are limited, creating a need for new methods.
  • The Full Bayesian Significance Test (FBST) has theoretical and practical advantages as a Bayesian alternative.

Purpose of the Study:

  • Introduce the fbst R package for implementing the Full Bayesian Significance Test (FBST).
  • Demonstrate FBST as a Bayesian alternative to NHST and p-values.
  • Provide practical examples of FBST application in cognitive science research.

Main Methods:

  • Implementation of the Full Bayesian Significance Test (FBST) using the fbst R package.
  • Calculation of the e-value, representing Bayesian evidence against a sharp null hypothesis.
  • Application to any Bayesian model with obtainable posterior distributions.

Main Results:

  • The fbst package provides the e-value for hypothesis testing.
  • It allows computation of asymptotic p-values and generates visualizations for interpretation.
  • Demonstrated practical application of FBST in three common cognitive science statistical procedures.

Conclusions:

  • The fbst R package offers a valuable Bayesian alternative for hypothesis testing.
  • FBST provides theoretical and practical benefits over traditional NHST.
  • Encourages wider adoption of FBST in cognitive science research for sharp null hypothesis testing.